Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self-adaptive particle swarm optimization and a clustering method

摘要

A novel model based on a radial basis function neural network (RBF NN), chaos theory, self-adaptive particle swarm optimization (PSO), and a clustering method is proposed to predict the gas solubility in polymers; this model is hereafter called CSPSO-C RBF NN. To develop the CSPSO-C RBF NN, the conventional PSO was modified with chaos theory and a self-adaptive inertia weight factor to overcome its premature convergence problem. The classical k-means clustering method was used to tune the hidden centers and radial basis function spreads, and the modified PSO algorithm was used to optimize the RBF NN connection weights. Then, the CSPSO-C RBF NN was used to investigate the solubility of N_2 in polystyrene (PS) and CO_2 in PS, polypropylene, poly(butylene succinate), and poly(butylene succinate-co-adipate). The results obtained in this study indicate that the CSPSO-C RBF NN was an effective method for predicting the gas solubility in polymers. In addition, compared with conventional RBF NN and PSO neural network, the CSPSO-C RBF NN showed better performance. The values of the average relative deviation, squared correlation coefficient, and standard deviation were 0.1282, 0.9970, and 0.0115, respectively. The statistical data demonstrated that the CSPSO-C RBF NN had excellent prediction capabilities with a high accuracy and a good correlation between the predicted values and the experimental data.